Databases
StackOverflow2013
Postsdbo
POUnderstanding LDA implementation using gensim
 

Note that there are some explanatory texts on larger screens.

plurals
  1. POUnderstanding LDA implementation using gensim
    primarykey
    data
    text
    <p>I am trying to understand how gensim package in Python implements Latent Dirichlet Allocation. I am doing the following:</p> <p>Define the dataset</p> <pre><code>documents = ["Apple is releasing a new product", "Amazon sells many things", "Microsoft announces Nokia acquisition"] </code></pre> <p>After removing stopwords, I create the dictionary and the corpus:</p> <pre><code>texts = [[word for word in document.lower().split() if word not in stoplist] for document in documents] dictionary = corpora.Dictionary(texts) corpus = [dictionary.doc2bow(text) for text in texts] </code></pre> <p>Then I define the LDA model.</p> <pre><code>lda = gensim.models.ldamodel.LdaModel(corpus=corpus, id2word=dictionary, num_topics=5, update_every=1, chunksize=10000, passes=1) </code></pre> <p>Then I print the topics:</p> <pre><code>&gt;&gt;&gt; lda.print_topics(5) ['0.181*things + 0.181*amazon + 0.181*many + 0.181*sells + 0.031*nokia + 0.031*microsoft + 0.031*apple + 0.031*announces + 0.031*acquisition + 0.031*product', '0.077*nokia + 0.077*announces + 0.077*acquisition + 0.077*apple + 0.077*many + 0.077*amazon + 0.077*sells + 0.077*microsoft + 0.077*things + 0.077*new', '0.181*microsoft + 0.181*announces + 0.181*acquisition + 0.181*nokia + 0.031*many + 0.031*sells + 0.031*amazon + 0.031*apple + 0.031*new + 0.031*is', '0.077*acquisition + 0.077*announces + 0.077*sells + 0.077*amazon + 0.077*many + 0.077*nokia + 0.077*microsoft + 0.077*releasing + 0.077*apple + 0.077*new', '0.158*releasing + 0.158*is + 0.158*product + 0.158*new + 0.157*apple + 0.027*sells + 0.027*nokia + 0.027*announces + 0.027*acquisition + 0.027*microsoft'] 2013-12-03 13:26:21,878 : INFO : topic #0: 0.181*things + 0.181*amazon + 0.181*many + 0.181*sells + 0.031*nokia + 0.031*microsoft + 0.031*apple + 0.031*announces + 0.031*acquisition + 0.031*product 2013-12-03 13:26:21,880 : INFO : topic #1: 0.077*nokia + 0.077*announces + 0.077*acquisition + 0.077*apple + 0.077*many + 0.077*amazon + 0.077*sells + 0.077*microsoft + 0.077*things + 0.077*new 2013-12-03 13:26:21,880 : INFO : topic #2: 0.181*microsoft + 0.181*announces + 0.181*acquisition + 0.181*nokia + 0.031*many + 0.031*sells + 0.031*amazon + 0.031*apple + 0.031*new + 0.031*is 2013-12-03 13:26:21,881 : INFO : topic #3: 0.077*acquisition + 0.077*announces + 0.077*sells + 0.077*amazon + 0.077*many + 0.077*nokia + 0.077*microsoft + 0.077*releasing + 0.077*apple + 0.077*new 2013-12-03 13:26:21,881 : INFO : topic #4: 0.158*releasing + 0.158*is + 0.158*product + 0.158*new + 0.157*apple + 0.027*sells + 0.027*nokia + 0.027*announces + 0.027*acquisition + 0.027*microsoft &gt;&gt;&gt; </code></pre> <p>I'm not able to understand much out of this result. Is it providing with a probability of the occurrence of each word? Also, what's the meaning of topic #1, topic #2 etc? I was expecting something more or less like the most important keywords.</p> <p>I already checked the <a href="http://radimrehurek.com/gensim/wiki.html#latent-dirichlet-allocation" rel="noreferrer">gensim tutorial</a> but it didn't really help much.</p> <p>Thanks.</p>
    singulars
    1. PO
      singulars
      1. PTAnswer
    1. This table or related slice is empty.
    1. PTQuestion
    1. UStusharmakkar08
    1. USvisakh
    plurals
    1. This table or related slice is empty.
    1. This table or related slice is empty.
    1. This table or related slice is empty.
    1. PO
      singulars
      1. PTAnswer
    1. VO
      singulars
      1. POUnderstanding LDA implementation using gensim
      1. This table or related slice is empty.
      1. VTUpMod
    1. This table or related slice is empty.
 

Querying!

 
Guidance
A row detail

Detail views are divided into sections. All the information in the data section comes from columns in the selected row. The other sections display data from other, related rows.

Related data can be related in a to-one or a to-many fashion. Captions of data related in a to-many fashion link to a list view showing a filtered view of the table.

Try moving around until you find a non-empty to-many entry and click on the label to get to one. You can move back to the root by clicking on the database name in the header.

SQuiL has stopped working due to an internal error.

If you are curious you may find further information in the browser console, which is accessible through the devtools (F12).

Reload